Multi-Modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and overlap with other psychiatric conditions complicate diagnosis, necessitating advanced systems. Existing research studies have mostly focused on imaging data, such as structural and functional MRI, for SZ diagnosis. There has been less focus on the integration of genomic features despite their potential in identifying heritable SZ traits. This study introduces a Multi-modal Imaging Genomics Transformer (MIGTrans) that integrates genomics with structural and functional imaging to identify SZ-related abnormalities. MIGTrans improved SZ classification accuracy to 86.05%, offering clear interpretations and identifying key genomic and brain morphological/connectivity patterns linked to SZ.

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Published
2025-04-14
DOI
https://doi.org/10.1109/isbi60581.2025.10980651
Primary Topic
Cell Image Analysis Techniques
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article
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article

Multi-Modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

Teja Krishna Cherukuri, Nagur Shareef Shaik, Vince D. Calhoun, Dong Hye Ye
Cell Image Analysis Techniques
article

Multi-Modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

Teja Krishna Cherukuri, Nagur Shareef Shaik, Vince D. Calhoun, Dong Hye Ye
article en

Abstract

Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and overlap with other psychiatric conditions complicate diagnosis, necessitating advanced systems. Existing research studies have mostly focused on imaging data, such as structural and functional MRI, for SZ diagnosis. There has been less focus on the integration of genomic features despite their potential in identifying heritable SZ traits. This study introduces a Multi-modal Imaging Genomics Transformer (MIGTrans) that integrates genomics with structural and functional imaging to identify SZ-related abnormalities. MIGTrans improved SZ classification accuracy to 86.05%, offering clear interpretations and identifying key genomic and brain morphological/connectivity patterns linked to SZ.

Georgia State University (US)
Openalex Percentile: Top 92%
Cell Image Analysis Techniques
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